On the other hand, when it comes to networks with unknown topology, a question that arises naturally is: “How can we identify a set of seed nodes that indeed necessitates the network structure?". Recent studies have addressed this question by allowing a small budget for node queries so that a part of the underlying network is explored, corresponding to network discovery, while aiming to find a set of seed nodes from the explored subgraph that is expected to be as influential as the globally optimal seed set [1]}, [2]}, [3]}, [4]}, [5]}. To aid the network discovery process, existing methods exploit rich complementary information such as the community structure [3]} or a set of similar graphs [4]}. However, such data are scarcely available under the topology-unaware setting of many real-world networks. Meanwhile, node metadata (i.e., node features) can be retrieved from various sources such as users’ created content and thus are much more readily collected than the relationship between individuals [8]}. For example, in co-authorship networks, we may take advantage of the keywords in each author's publications to infer a potential relationship between authors if their papers contain similar keywords. Although attempts have been made to exploit certain forms of difficult-to-collect node metadata [5]}, the metadata collection under consideration is entirely handcrafted, requiring a significant annotation effort by domain experts.
